class_docstr: This ``SimpleTagger`` simply encodes a sequence of text with a stacked ``Seq2SeqEncoder``, then
  predicts a tag for each token in the sequence.
  
  Parameters
  ----------
  vocab : ``Vocabulary``, required
      A Vocabulary, required in order to compute sizes for input/output projections.
  text_field_embedder : ``TextFieldEmbedder``, required
      Used to embed the ``tokens`` ``TextField`` we get as input to the model.
  encoder : ``Seq2SeqEncoder``
      The encoder (with its own internal stacking) that we will use in between embedding tokens
      and predicting output tags.
  initializer : ``InitializerApplicator``, optional (default=``InitializerApplicator()``)
      Used to initialize the model parameters.
  regularizer : ``RegularizerApplicator``, optional (default=``None``)
      If provided, will be used to calculate the regularization penalty during training.
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code: def __init__(
      self,
      vocab,
      text_field_embedder,
      encoder,
      initializer=InitializerApplicator(),
      regularizer=None,
  ):
      super(SimpleTagger, self).__init__(vocab, regularizer)
  
      self.text_field_embedder = text_field_embedder
      self.num_classes = self.vocab.get_vocab_size("labels")
      self.encoder = encoder
      self.tag_projection_layer = TimeDistributed(
          Linear(self.encoder.get_output_dim(), self.num_classes)
      )
  
      check_dimensions_match(
          text_field_embedder.get_output_dim(),
          encoder.get_input_dim(),
          "text field embedding dim",
          "encoder input dim",
      )
      self.metrics = {
          "accuracy": CategoricalAccuracy(),
          "accuracy3": CategoricalAccuracy(top_k=3),
      }
  
      initializer(self)
  
file_path: magnitude/pymagnitude/third_party/allennlp/models/simple_tagger.py
imports: from __future__ import absolute_import
  import numpy
  import torch
  from torch.nn.modules.linear import Linear
  import torch.nn.functional
  from allennlp.common.checks import check_dimensions_match
  from allennlp.data import Vocabulary
  from allennlp.modules import Seq2SeqEncoder, TimeDistributed, TextFieldEmbedder
  from allennlp.models.model import Model
  from allennlp.nn import InitializerApplicator, RegularizerApplicator
  from allennlp.nn.util import get_text_field_mask, sequence_cross_entropy_with_logits
  from allennlp.training.metrics import CategoricalAccuracy
label_desc: Use image node_id 1 to create a new SimpleTagger object from inherited base classes: Model with example: obj = SimpleTagger(vocab, text_field_embedder, encoder, initializer, regularizer)